Ten to 25% of people with late-onset Alzheimer's disease (AD) present with prominent executive deficits. Names such as frontal variant AD, executive prominent AD, and dysexecutive AD have been applied to this phenomenon. Little is known about traditional or genetic risk factors for dysexecutive AD. The overarching goal of this project is to further our understanding of the genetic architecture and clinical epidemiology of dysexecutive AD in the hopes of ultimately developing disease- modifying treatments. This project will leverage large-scale genome-wide genotype and sequence data and cognitive data collected on >17,000 participants across 19 collaborating studies. The investigators will use modern psychometric methods to co-calibrate cognitive data to develop scores on the same metric for memory and executive functioning. The investigators will use these scores to determine a continuous dysexecutive spectrum phenotype they have found to be highly heritable, with a pattern of heritability entirely distinct from that of AD. The investigators will leverage genome-wide genotype data for Aim 1. Five of the collaborating studies are prospective cohort studies with extensive cognitive and clinical data from >10,000 participants. The investigators will leverage these data for Aim 2. Several funding mechanisms are producing whole genome and whole exome sequencing data for people with AD. The investigators will leverage these data for Aim 3. Taken together, these investigations promise to improve what is known about dysexecutive AD, a highly heritable and devastating AD subtype. This work may identify genetic loci associated with risk for dysexecutive AD, which in turn may lead to development of drugs that could dramatically improve the lives of people with this condition.
The investigators propose to leverage extensive genetic, clinical, and cognitive data from 19 collaborating studies with >17,000 participants with Alzheimer's disease (AD) to further the understanding of dysexecutive AD, a devastating AD subtype. A series of genetic and epidemiological investigations are proposed. These investigations would dramatically increase the state of knowledge of this AD subtype, and may hasten the development of disease-modifying drugs.
|Crane, Paul K; Trittschuh, Emily; Mukherjee, Shubhabrata et al. (2017) Incidence of cognitively defined late-onset Alzheimer's dementia subgroups from a prospective cohort study. Alzheimers Dement 13:1307-1316|
|Mukherjee, Shubhabrata; Russell, Joshua C; Carr, Daniel T et al. (2017) Systems biology approach to late-onset Alzheimer's disease genome-wide association study identifies novel candidate genes validated using brain expression data and Caenorhabditis elegans experiments. Alzheimers Dement 13:1133-1142|
|Li, Huang; Fang, Shiaofen; Contreras, Joey A et al. (2017) Brain explorer for connectomic analysis. Brain Inform 4:253-269|
|Du, Lei; Liu, Kefei; Yao, Xiaohui et al. (2017) Pattern Discovery in Brain Imaging Genetics via SCCA Modeling with a Generic Non-convex Penalty. Sci Rep 7:14052|
|Yao, Xiaohui; Yan, Jingwen; Risacher, Shannon et al. (2017) NETWORK-BASED GENOME WIDE STUDY OF HIPPOCAMPAL IMAGING PHENOTYPE IN ALZHEIMER'S DISEASE TO IDENTIFY FUNCTIONAL INTERACTION MODULES. Proc IEEE Int Conf Acoust Speech Signal Process 2017:6170-6174|
|Fardo, David W; Gibbons, Laura E; Mukherjee, Shubhabrata et al. (2017) Impact of home visit capacity on genetic association studies of late-onset Alzheimer's disease. Alzheimers Dement 13:933-939|
|Liu, Kefei; Yao, Xiaohui; Yan, Jingwen et al. (2017) Transcriptome-Guided Imaging Genetic Analysis via a Novel Sparse CCA Algorithm. Graphs Biomed Image Anal Comput Anat Imaging Genet (2017) 10551:220-229|
|Hao, Xiaoke; Li, Chanxiu; Yan, Jingwen et al. (2017) Identification of associations between genotypes and longitudinal phenotypes via temporally-constrained group sparse canonical correlation analysis. Bioinformatics 33:i341-i349|
|Hao, Xiaoke; Li, Chanxiu; Du, Lei et al. (2017) Mining Outcome-relevant Brain Imaging Genetic Associations via Three-way Sparse Canonical Correlation Analysis in Alzheimer's Disease. Sci Rep 7:44272|
|Yao, Xiaohui; Yan, Jingwen; Kim, Sungeun et al. (2017) Two-dimensional enrichment analysis for mining high-level imaging genetic associations. Brain Inform 4:27-37|
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